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• W4385444734 abstract "Plug-and-play priors are popular for solving illposed imaging inverse problems. Recent efforts indicate that the convergence guarantee of the imaging algorithms using plug-and-play priors relies on the assumption of bounded denoisers. However, the bounded properties of existing plugged Gaussian denoisers have not been proven explicitly. To bridge this gap, we detail a novel provable bounded denoiser termed as BMDual, which combines a trainable denoiser using dual tight frames and the well-known block-matching and 3D filtering (BM3D) denoiser. We incorporate multiple dual frames utilized by BMD-ual into a novel regularization model induced by a solver. The proposed regularization model is utilized for compressed sensing magnetic resonance imaging (CSMRI). We theoretically show the bound of the BMDual denoiser, the bounded gradient of the CSMRI data-fidelity function, and further demonstrate that the proposed CSMRI algorithm converges. Experimental results also demonstrate that the proposed algorithm has a good convergence behavior, and show the effectiveness of the proposed algorithm." @default.
• W4385444734 created "2023-08-02" @default.
• W4385444734 creator A5035934562 @default.
• W4385444734 creator A5069826751 @default.
• W4385444734 date "2023-11-01" @default.
• W4385444734 modified "2023-09-30" @default.
• W4385444734 title "Regularization by multiple dual frames for compressed sensing magnetic resonance imaging with convergence analysis" @default.
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• W4385444734 doi "https://doi.org/10.1109/jas.2023.123543" @default.
• W4385444734 hasPublicationYear "2023" @default.
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